{"doi":"10.1148/ryai.2021200127","title":"Optimizing Deep Learning Algorithms for Segmentation of Acute Infarcts on Non–Contrast Material–enhanced CT Scans of the Brain Using Simulated Lesions","abstract":"Purpose To test the efficacy of lesion segmentation using a deep learning algorithm on non–contrast material–enhanced CT (NCCT) images with synthetic lesions resembling acute infarcts. Materials and Methods In this retrospective study, 40 diffusion-weighted imaging (DWI) lesions in patients with acute stroke (median age, 69 years; range, 62–76 years; 17 women; screened between 2011 and 2017) were coregistered to 40 normal NCCT scans (median age, 70 years; range, 55–76 years; 25 women; screened between 2008 and 2011), which produced 640 combinations of DWI-NCCT with and without lesions for training (n = 420), validation (n = 110), and testing (n = 110). The signal intensity on the NCCT scans was depressed by 4 HU (a 13% drop) in the region of the diffusion-weighted lesion. Two U-Net architectures (standard and symmetry aware) were trained with two different training strategies. One was a naive strategy, in which the model started training with random coefficients. The other was a progressive strategy, which started with coefficients derived from a model trained on a dataset with lesions that were depressed by 10 HU. The Dice scores from the two architectures and training strategies were compared from the test dataset. Results Dice scores of symmetry-aware U-Nets were 25% higher than those of standard U-Nets (median, 0.49 vs 0.65; P < .001). Use of a progressive training strategy had no clear effect on model performance. Conclusion Symmetry-aware U-Nets offer promise for segmentation of acute stroke lesions on NCCT scans. Keywords: Adults, CT-Quantitative, Stroke Supplemental material is available for this article. © RSNA, 2021","journal":"Radiology Artificial Intelligence","year":2021,"id":200041,"datarank":0.43003665738909314,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.10045297078866017,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.10045297078866017,"corpus_percentile":56.73396766457802,"corpus_rank":5594,"citation_count":8,"citer_count":7,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5158,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":545437,"name":"Michael Mlynash","orcid":"0000-0001-7483-8699","position":1,"is_corresponding":false},{"id":395215,"name":"Julian Maclaren","orcid":"0000-0001-7073-2926","position":2,"is_corresponding":false},{"id":545439,"name":"Christian Federau","orcid":"0000-0002-3803-6602","position":3,"is_corresponding":false},{"id":252224,"name":"Gregory W. Albers","orcid":"0000-0003-0263-4632","position":4,"is_corresponding":false},{"id":252223,"name":"Maarten G. Lansberg","orcid":"0000-0002-3545-6927","position":5,"is_corresponding":false},{"id":252221,"name":"Sören Christensen","orcid":"0000-0003-1242-3724","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":null,"created_at":"2026-07-18T23:50:44.479665Z","pmid":"34350404","pmcid":"PMC8328101","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}